National Yang Ming Chiao Tung University (NYCU) brings together Information Management and Finance to form an interdisciplinary program that integrates information engineering, data analytics, and financial strategy. By embedding AI technologies into both research and education, the institute has built an intelligent financial research platform that enables large-scale data analysis, quantitative modeling, and real-world strategy development—empowering students and researchers to accelerate innovation and data-driven decision-making.
During the development of its AI-driven financial research and teaching platform, the team needed to frequently access massive volumes of historical trading data and analytics results, making storage performance a critical factor in overall computing efficiency.
To meet the performance and stability demands of AI-driven chip analysis and strategy execution, NYCU implemented a dual-tier architecture that combines QSAN high-performance storage with unified storage, effectively isolating different workloads for optimal efficiency. All-flash storage is dedicated to performance-intensive tasks, while unified storage supports shared data services.
QSAN XF Series All-NVMe Flash Storage: Designed for SQL and database servers, it powers AI analytics, strategy backtesting, and core execution data. With high-bandwidth fiber connectivity and MPIO (multi-path I/O), the system ensures ultra-low latency and high availability.
QSAN XN Series Unified Storage: Centralizes research datasets, analytics results, and model files, delivering stable, flexible, and efficient file-sharing services.
Performance and Access Segmentation: Block storage and file services operate independently, preventing interference between AI computing workloads and user access.
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